Wed@HIC: Meet Our Fellows "New Data from Old Sources: A Machine Learning Approach to Census Record Linking"
- Starts: 3:15 pm on Wednesday, January 22, 2020
- Ends: 5:00 pm on Wednesday, January 22, 2020
James Feigenbaum, Assistant Professor, Economics, College & Grad School of Arts & Sciences The recent digitization of complete count census data is an extraordinary opportunity for social scientists to create large longitudinal datasets by linking individuals from one census to another or from other sources to the census. However, linking with simple algorithms is challenging when data is enumerated and transcribed with error and names are common and changing over time and hand linking, though accurate, is expensive, slow, and not replicable. I will present a machine learning approach that trains on the actual matches made by a skilled researcher or genealogist to make implicit linking rules explicit. In addition, I will present preliminary results from two new projects exploiting linked data to demonstrate the possibilities of the complete count historical censuses.
- Economics Building, 270 Bay State Road. The reception is taking place on the 4th floor in the lounge, Room 416. The talk is taking place in SSW Room 315.